We compared our proposed approach with nine different state of the art methods namely Li et al. [1]}, NISP [2]}, DCP-A [3]}, CP [4]}, AMC [5]}, C-SGD [6]}, GBN-40 [7]}, GBN-30 [7]} and GAN Pruning [9]}. Table REF  shows the comparison with these methods on CIFAR-10 datasets on VGG-16 architecture. It shows the comparison of our filter pruning and filter and layer pruning techniques vs. generative adversarial network [9]}. Both our techniques performed better than SOA without noticeable loss in accuracy. Our filter and layer pruning resulted in an increase of \(7.46\%\)  in parameter compression ratio and \(12.59\%\)  increase in flop compression ratio.
